A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization
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Updated
Apr 28, 2024
A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization
Official PyTorch implementation of Fully Attentional Networks
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.
[ICLR 2022] Official pytorch implementation of "Uncertainty Modeling for Out-of-Distribution Generalization" in International Conference on Learning Representations (ICLR) 2022.
ImageNet-R(endition) and DeepAugment (ICCV 2021)
[ICML 2023] Change is Hard: A Closer Look at Subpopulation Shift
A project to add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Perform efficient inferences (i.e., do not increase inference time) and detection without classification accuracy drop, hyperparameter tuning, or collecting additional data.
The Official Implementation of the ICCV-2021 Paper: Semantically Coherent Out-of-Distribution Detection.
OOD Generalization and Detection (ACL 2020)
[KDD 2022] "Causal Attention for Interpretable and Generalizable Graph Classification" by Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, Xiangnan He, Tat-Seng Chua.
[ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"
[CVPR2024 Highlight] Official Code for "ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object"
A project to improve out-of-distribution detection (open set recognition) and uncertainty estimation by changing a few lines of code in your project! Perform efficient inferences (i.e., do not increase inference time) without repetitive model training, hyperparameter tuning, or collecting additional data.
Distilling Large Vision-Language Model with Out-of-Distribution Generalizability (ICCV 2023)
A project to train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.
Repository for the paper "Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey"
AAAI 2021. Neural Sequence-to-grid Module for Learning Symbolic Rules
[CVPR 2023] Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection
Scripts, figures and working notes for the participation in FungiCLEF-2022, part of the 13th CLEF Conference, 2022
Official code and data for NeurIPS 2023 paper "ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification"
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